Water Quality and Potability

We are collecting a dataset on water quality to train a machine learning model for binary classification: determining whether water is safe for consumption (1) or not (0). This model will help with water treatment decisions and ensure compliance with quality standards. We applied different summarization and plotting methods to help us to understand our dataset, such as scatter, histogram and bar plot. Then, we applyed preprocess in our data using data cleaning, data transformation and feature selection.

#library:
#install.packages("caret")
#install.packages("glmnet")
#install.packages("Boruta")
#install.packages("mlbench")
#install.packages("randomForest")
library(outliers)
library(dplyr)
library(mlbench)
library(caret)
library(glmnet)
library(Boruta)
library(ggplot2)
library(randomForest)

getwd()
#setwd("/Users/mahayie/Desktop/326p")
#getwd()

water_potability = read.csv('Dataset/water_potability.csv')

View(water_potability)

str(water_potability)
summary(water_potability)

Checking for missing values:

dim(water_potability)

[1] 3276   10

sum(is.na(water_potability))

[1] 1434

Remove rows with missing values

water_potability = na.omit(water_potability)
View(water_potability)

Description: The absence of data in certain variables or columns in a dataset is referred to as missing or null values due to various reasons. It can have a negative impact on the dataset’s efficiency and the information that can be taken from it later, so we checked to see whether our data had missing or null values and eliminated these rows to produce a more efficient dataset.

Standard deviation:

sd(water_potability$Turbidity)

[1] 0.7803462

sd(water_potability$Solids)

[1] 8642.24

sd(water_potability$Conductivity)

[1] 80.71257

sd(water_potability$Organic_carbon)

[1] 3.324959

sd(water_potability$ph)

[1] 1.573337

Mean:

mean(water_potability$Turbidity)

[1] 3.969729

mean(water_potability$Solids) 

[1] 21917.44

mean(water_potability$Conductivity) 

[1] 426.5264

mean(water_potability$Organic_carbon) 

[1] 14.35771

mean(water_potability$ph) 

[1] 7.08599

Median

median(water_potability$Turbidity)

[1] 3.968177

median(water_potability$Solids)

[1] 20933.51

median(water_potability$Conductivity)

[1] 423.4559

median(water_potability$Organic_carbon)

[1] 14.32202

median(water_potability$ph)

[1] 7.027297

Variance

var(water_potability$Turbidity)

[1] 0.6089401

var(water_potability$Solids)

[1] 74688309

var(water_potability$Conductivity)

[1] 6514.519

var(water_potability$Organic_carbon)

[1] 11.05535

var(water_potability$ph)

[1] 2.475388

Statistical Measures:

summary(water_potability$Conductivity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  201.6   366.7   423.5   426.5   482.4   753.3 

summary(water_potability$Organic_carbon)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2.20   12.12   14.32   14.36   16.68   27.01 

summary(water_potability$Hardness)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  73.49  176.74  197.19  195.97  216.44  317.34 

summary(water_potability$Solids)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  320.9 15615.7 20933.5 21917.4 27182.6 56488.7 

summary(water_potability$Chloramines)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.391   6.139   7.144   7.134   8.110  13.127 

summary(water_potability$Potability)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4033  1.0000  1.0000 

summary(water_potability$Sulfate)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  129.0   307.6   332.2   333.2   359.3   481.0 

summary(water_potability$Trihalomethanes)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  8.577  55.953  66.542  66.401  77.292 124.000 

summary(water_potability$Turbidity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.450   3.443   3.968   3.970   4.514   6.495 

summary(water_potability$ph)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2275  6.0897  7.0273  7.0860  8.0530 14.0000 

Descriotion: With using minimum, maximum, mean, median laws it helps to provide an overview of the data’s key characteristics

outliers before removing outlier:

dim(water_potability)

[1] 2011   10

head(water_potability)

removing outliers:

• ph

summary(water_potability$ph)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2275  6.0897  7.0273  7.0860  8.0530 14.0000 

quartiles <- quantile(water_potability$ph, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
6.089723 8.052969 

iqr <- IQR(water_potability$ph)
iqr

[1] 1.963245

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
3.144855 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
10.99784 

boxplot(ph ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$ph, ylab = 'ph')$out
  out_val
  out_rows <- which(water_potability$ph %in% c(out_val))
  out_rows
  
  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$ph)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.231   6.105   7.027   7.087   8.030  10.905 

#-------------------------------------------

-Hardness

summary(water_potability$Hardness)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  73.49  176.90  197.36  196.27  216.44  317.34 

quartiles <- quantile(water_potability$Hardness, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
176.9031 216.4411 

iqr <- IQR(water_potability$Hardness)
iqr

[1] 39.53799

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
117.5961 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
275.7481 

boxplot(Hardness ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Hardness, ylab = 'Hardness')$out
  out_val
  out_rows <- which(water_potability$Hardness %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Hardness)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  121.0   177.7   197.3   196.2   215.5   272.1 

#-------------------------------------------

-Solids

summary(water_potability$Solids)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  320.9 15704.5 20855.3 21840.2 27045.9 56488.7 

quartiles <- quantile(water_potability$Solids, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
15704.48 27045.93 

iqr <- IQR(water_potability$Solids)
iqr

[1] 11341.45

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
-1307.69 

upper <- quartiles[2] + 1.5*iqr
upper

    75% 
44058.1 

boxplot(Solids ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Solids, ylab = 'Solids')$out
  out_val
  out_rows <- which(water_potability$Solids %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Solids)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  320.9 15547.5 20518.7 21419.6 26734.7 43195.5 

#-------------------------------------------

-Chloramines

summary(water_potability$Chloramines)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.391   6.141   7.135   7.135   8.094  13.127 

quartiles <- quantile(water_potability$Chloramines, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
6.141236 8.094323 

iqr <- IQR(water_potability$Chloramines)
iqr

[1] 1.953087

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
3.211605 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
11.02395 

boxplot(Chloramines ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Chloramines, ylab = 'Chloramines')$out
  out_val
  out_rows <- which(water_potability$Chloramines %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Chloramines)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.352   6.181   7.137   7.136   8.076  10.897 

#-------------------------------------------

-Sulfate

summary(water_potability$Sulfate)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  187.2   308.2   332.6   333.4   358.3   481.0 

quartiles <- quantile(water_potability$Sulfate, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
308.1884 358.3020 

iqr <- IQR(water_potability$Sulfate)
iqr

[1] 50.11358

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
233.0181 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
433.4724 

boxplot(Sulfate ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Sulfate, ylab = 'Sulfate')$out
  out_val
  out_rows <- which(water_potability$Sulfate %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Sulfate)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  237.5   309.2   332.8   333.6   357.7   429.8 

#-------------------------------------------

-Conductivity

summary(water_potability$Conductivity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  201.6   366.6   423.6   426.8   482.6   753.3 

quartiles <- quantile(water_potability$Conductivity, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
366.5581 482.5983 

iqr <- IQR(water_potability$Conductivity)
iqr

[1] 116.0401

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
192.4979 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
656.6585 

boxplot(Conductivity ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Conductivity, ylab = 'Conductivity')$out
  out_val
  out_rows <- which(water_potability$Conductivity %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Conductivity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  201.6   366.4   423.1   426.0   481.9   652.5 

#-------------------------------------------

-Organic_carbon

summary(water_potability$Organic_carbon)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  4.372  12.184  14.351  14.417  16.788  27.007 

quartiles <- quantile(water_potability$Organic_carbon, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
12.18447 16.78779 

iqr <- IQR(water_potability$Organic_carbon)
iqr

[1] 4.603315

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
5.279502 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
23.69276 

boxplot(Organic_carbon ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Organic_carbon, ylab = 'Organic_carbon')$out
  out_val
  out_rows <- which(water_potability$Organic_carbon %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Organic_carbon)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  5.512  12.222  14.352  14.426  16.786  23.604 

#-------------------------------------------

-Trihalomethanes

summary(water_potability$Trihalomethanes)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  8.577  55.865  66.231  66.364  77.418 124.000 

quartiles <- quantile(water_potability$Trihalomethanes, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
55.86494 77.41789 

iqr <- IQR(water_potability$Trihalomethanes)
iqr

[1] 21.55295

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
23.53552 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
109.7473 

boxplot(Trihalomethanes ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Trihalomethanes, ylab = 'Trihalomethanes')$out
  out_val
  out_rows <- which(water_potability$Trihalomethanes %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Trihalomethanes)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  24.53   55.96   66.29   66.42   77.34  108.85 

#-------------------------------------------

-Turbidity

summary(water_potability$Turbidity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.450   3.441   3.975   3.973   4.519   6.495 

quartiles <- quantile(water_potability$Turbidity, probs = c(.25, .75), na.rm = FALSE)
quartiles

     25%      75% 
3.440859 4.518751 

iqr <- IQR(water_potability$Turbidity)
iqr

[1] 1.077892

lower <- quartiles[1] - 1.5*iqr
lower

     25% 
1.824021 

upper <- quartiles[2] + 1.5*iqr
upper

     75% 
6.135588 

boxplot(Turbidity ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Turbidity, ylab = 'Turbidity')$out
  out_val
  out_rows <- which(water_potability$Turbidity %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Turbidity)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.873   3.443   3.974   3.972   4.512   6.084 

After removing outliers:

dim(water_potability)

[1] 1750   10

str(water_potability)

'data.frame':   1750 obs. of  10 variables:
 $ ph             : num  8.32 9.09 5.58 10.22 8.64 ...
 $ Hardness       : num  214 181 188 248 203 ...
 $ Solids         : num  22018 17979 28749 28750 13672 ...
 $ Chloramines    : num  8.06 6.55 7.54 7.51 4.56 ...
 $ Sulfate        : num  357 310 327 394 303 ...
 $ Conductivity   : num  363 398 280 284 475 ...
 $ Organic_carbon : num  18.4 11.6 8.4 13.8 12.4 ...
 $ Trihalomethanes: num  100.3 32 54.9 84.6 62.8 ...
 $ Turbidity      : num  4.63 4.08 2.56 2.67 4.4 ...
 $ Potability     : int  0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "na.action")= 'omit' Named int [1:1265] 1 2 3 9 12 14 15 17 19 21 ...
  ..- attr(*, "names")= chr [1:1265] "1" "2" "3" "9" ...

head(water_potability)

Description: Removing outliers from a dataset is critical for assuring the quality and reliability of statistical analysis and machine learning models. We found all outliers in the numerical attributes and subsequently eliminated the rows containing the outliers.

Charts

Histogram

hist(water_potability$ph)

hist(water_potability$Chloramines)

hist(water_potability$Hardness)

hist(water_potability$Solids)

hist(water_potability$Sulfate)

hist(water_potability$Conductivity)

hist(water_potability$Organic_carbon)

hist(water_potability$Trihalomethanes)

hist(water_potability$Turbidity)

Bar Plot

tab <- water_potability$Potability %>% table()
txt <- paste0(tab) 
bb <- water_potability$ph %>% table() %>% barplot( main='ph',col=c('pink'))

bb <- water_potability$Potability %>% table() %>% barplot( main='Potability',ylab='Frequency',col=c('pink', 'lightblue'))
text(bb, tab/2, labels=txt, cex=1)

Pie chart

water_potability$Potability %>% table() %>% pie()

Scatter Plot

with(water_potability, plot(Turbidity, ph, col = Potability, pch = as.numeric(Potability)))

Description: -Histogram: The histogram shows the frequency of ph in the dataset; we noted that the majority of values fall within the usual range, which is about between 6 and 8, but it also shows several outliers. -Scatter plot: This scatter demonstrates the correlation and proportionality between the two qualities, allowing us to establish whether or not turbidity and pH are connected. -Bar Plot the bar plot represent how ph levels affect water portability in the dataset it indicates that ph level above 10 is not portibal and humans cant consume it

Remove Redundant Features:

correlation_matrix <- cor(water_potability[,1:9])
high_correlation_features <- findCorrelation(correlation_matrix, cutoff = 0.5)
print(high_correlation_features)

integer(0)

heatmap(correlation_matrix)

Description: This will find the correlation between the features and represent it in heat map

Feature selection

Rank Features By Importance:

#train random forest model and calculate feature importance
rf = randomForest(x= water_potability[,1:9],y= water_potability[,10])
var_imp <- varImp(rf, scale = FALSE)
#sort the score in decreasing order
var_imp_df <- data.frame(cbind(variable = rownames(var_imp), score = var_imp[,1]))
var_imp_df$score <- as.double(var_imp_df$score)
var_imp_df[order(var_imp_df$score,decreasing = TRUE),]

ggplot(var_imp_df, aes(x=reorder(variable, score), y=score)) + 
  geom_point() +
  geom_segment(aes(x=variable,xend=variable,y=0,yend=score)) +
  ylab("IncNodePurity") +
  xlab("Variable Name") +
  coord_flip()

Recursive Feature elimination:

control <- rfeControl(functions=rfFuncs, method="cv",number=10)
rf <- trainControl(method = "cv", number = 10, verboseIter = FALSE)
# run the RFE algorithm
rfe_model <- rfe(x= water_potability[,1:9],y= water_potability[,10], sizes=c(1:9), rfeControl=control)
# summarize the results
print(rfe_model)

Recursive feature selection

Outer resampling method: Cross-Validated (10 fold) 

Resampling performance over subset size:

The top 5 variables (out of 5):
   Sulfate, ph, Hardness, Solids, Chloramines

# list the chosen features
predictors(rfe_model)

[1] "Sulfate"     "ph"          "Hardness"    "Solids"      "Chloramines"

# plot the results
plot(rfe_model, type=c("g", "o"))

Description: ranking features by importance is a technique used to identify the most influential variables in a dataset for predicting a target variable. This process helps in understanding which features have the most impact on the model’s performance. By ranking features by importance.

removing redundant features refers to the process of eliminating variables or features from a dataset that do not provide any additional or unique information.

Data transformation

Normlization

normalize=function(x){return ((x-min(x))/(max(x)))}
w = water_potability

water_potability$Sulfate=normalize(water_potability$Sulfate)
water_potability$Conductivity=normalize(water_potability$Conductivity)
water_potability$Turbidity=normalize(water_potability$Turbidity)
water_potability$ph=normalize(water_potability$ph)
water_potability$Chloramines=normalize(water_potability$Chloramines)
water_potability$Solids=normalize(water_potability$Solids)
water_potability$Trihalomethanes=normalize(water_potability$Trihalomethanes)
water_potability$Organic_carbon=normalize(water_potability$Organic_carbon)
water_potability$Hardness=water_potability$Hardness/1000

water_potability$Hardness<-normalize(water_potability$Hardness)
print(water_potability)

Description: Normalization refers to the process of scaling variables to have a common range. It helps in comparing variables with different scales. In the solids attribute will create critical challenges since of the huge and diverted values(min=320,9 max=43195.5) so we normalized the solids to make values smaller and more reasonable. Also we normalized all the scaled attributes:Sulfate,Conductivity,Organic_corbon, Trihalomerhanes, Turbidity,ph,Chloramines.

Discretization:

w$Trihalomethanes= cut(w$Trihalomethanes, breaks = seq(0,125,by=25),right=FALSE)
w$Solids= cut(w$Solids, breaks = seq(0,50000,by=10000),right=FALSE)
w$Organic_carbon= cut(w$Organic_carbon, breaks = seq(0,25,by=5),right=FALSE)
print(w)

Description: Discretization is the process of transforming continuous variables into discrete or categorical variables. It’s can be useful for analyzing data that has a large number of unique values or when you want to simplify the data. So In Trihalomethanes we intervals by dividing the values by 25 to have a labels with equal width : (0,25],(25,50],(50,75],(75,100],(100,125].

Encoding encoding is the process of converting characters or strings into a specific encoding format. Since we don’t have a Nominal attribute in our database we couldn’t implement it.